323,004 research outputs found
An Ensemble of Hyperdimensional Classifiers: Hardware-Friendly Short-Latency Seizure Detection with Automatic iEEG Electrode Selection.
No full textWe propose an intracranial electroencephalography (iEEG) based algorithm for detecting epileptic seizures with short latency, and with identifying the most relevant electrodes. Our algorithm first extracts three features, namely mean amplitude, line length, and local binary patterns that are fed to an ensemble of classifiers using hyperdimensional (HD) computing. These features are embedded into an HD space where well-defined vector-space operations are used to construct prototype vectors representing ictal (during seizures) and interictal (between seizures) brain states. Prototype vectors can be computed at different spatial scales ranging from a single electrode up to many electrodes covering different brain regions. This flexibility allows our algorithm to identify the iEEG electrodes that discriminate best between ictal and interictal brain states. We assess our algorithm on the SWEC-ETHZ iEEG dataset that includes 99 short-time iEEG seizures recorded with 36 to 100 electrodes from 16 drug-resistant epilepsy patients. Using k-fold cross-validation and all electrodes, our algorithm surpasses state-of-the-art algorithms yielding significantly shorter latency (8.81 s vs. 9.94 s) in seizure onset detection, and higher sensitivity (96.38 % vs. 92.72 %) and accuracy (96.85 % vs. 95.43 %). We can further reduce the latency of our algorithm to 3.74 s by allowing a slightly higher percentage of false alarms (2 % specificity loss). Using only the top 10 % of the electrodes ranked by our algorithm, we still maintain superior latency, sensitivity, and specificity compared to the other algorithms with all the electrodes. We finally demonstrate the suitability of our algorithm to deployment on low-cost embedded hardware platforms, thanks to its robustness to noise/artifacts affecting the signal, its low computational complexity, and the small memory-footprint on a RISC-V microcontroller
Low-latency detection of epileptic seizures from IEEG with temporal convolutional networks on a low-power parallel MCU
No full textEpilepsy is a severe neurological disorder that affects about 1% of the world population, and one-third of cases are drug-resistant. Apart from surgery, drug-resistant patients can benefit from closed-loop brain stimulation, eliminating or mitigating the epileptic symptoms. For the closed-loop to be accurate and safe, it is paramount to couple stimulation with a detection system able to recognize seizure onset with high sensitivity and specificity and short latency, while meeting the strict computation and energy constraints of always-on real-time monitoring platforms. We propose a novel setup for iEEG-based epilepsy detection, exploiting a Temporal Convolutional Network (TCN) optimized for deployability on low-power edge devices for real-time monitoring. We test our approach on the Short-Term SWEC-ETHZ iEEG Database, containing a total of 100 epileptic seizures from 16 patients (from 2 to 14 per patient) comparing it with the state-of-the-art (SoA) approach, represented by Hyperdimensional Computing (HD). Our TCN attains a detection delay which is 10 s better than SoA, without performance drop in sensitivity and specificity. Contrary to previous literature, we also enforce a time-consistent setup, where training seizures always precede testing seizures chronologically. When deployed on a commercial low-power parallel microcontroller unit (MCU), each inference with our model has a latency of only 5.68 ms and an energy cost of only 124.5 μJ if executed on 1 core, and latency 1.46 ms and an energy cost 51.2 μJ if parallelized on 8 cores. These latency and energy consumption, lower than the current SoA, demonstrates the suitability of our solution for real-time long-term embedded epilepsy monitoring
Temporal Variability Analysis in sEMG Hand Grasp Recognition using Temporal Convolutional Networks
No full textHand movement recognition via surface electromyographic (sEMG) signal is a promising approach for the advance in Human-Computer Interaction. However, this field has to deal with two main issues: (1) the long-term reliability of sEMG-based control is limited by the variability affecting the sEMG signal (especially, variability over time); (2) the classification algorithms need to be suitable for implementation on embedded devices, which have strict constraints in terms of power budget and computational resources. Current solutions present a performance over-time drop that makes them unsuitable for reliable gesture controller design. In this paper, we address temporal variability of sEMG-based grasp recognition, proposing a new approach based on Temporal Convolutional Networks, a class of deep learning algorithms particularly suited for time series analysis and temporal pattern recognition. Our approach improves by 7.6% the best results achieved in the literature on the NinaPro DB6, a reference dataset for temporal variability analysis of sEMG. Moreover, when targeting the much more challenging inter-session accuracy objective, our method achieves an accuracy drop of just 4.8% between intra- and inter-session validation. This proves the suitability of our setup for a robust, reliable long-term implementation. Furthermore, we distill the network using deep network quantization and pruning techniques, demonstrating that our approach can use down to 120 lower memory footprint than the initial network and 4 lower memory footprint than a baseline Support Vector Machine, with an inter-session accuracy degradation of only 2.5%, proving that the solution is suitable for embedded resource-constrained implementations
GUT MICROBIOTA CROSSTALK WITH CONVENTIONAL AND NON-CONVENTIONAL T CELLS: A GAME OF MANY PLAYERS.
No full textThe presence of microbial commensals in the gut requires the establishment of a complex network of reciprocal interactions between the microbiota and the host immune system to allow nutrient absorption while preventing undesired mucosal immune responses. Despite these homeostatic mechanisms, during intestinal inflammation alterations of the microbiota composition, namely dysbiosis, trigger abnormal immune responses.
Here, we aimed at investigating the functional crosstalk between gut microbiota and the mucosal immune system during inflammation and upon induction of microbial dysbiosis.
We observed that inflammation-induced and antibiotic-driven types of dysbiosis are phenotypically and functionally modifying CD4+ T and iNKT cells activity. Moreover, during intestinal inflammation, the experimental manipulation of the microbiota community through Faecal Microbiota Transplantation (FMT) reduces colonic inflammation and initiates the restoration of intestinal homeostasis through the induction of IL-10 production by immune cells.
Further, we performed a comprehensive analysis on intestinal iNKT cells isolated from surgical specimens of active Inflammatory Bowel Disease (IBD) patients and non-IBD donors. We report that the exposure to mucosa-associated microbiota drives iNKT cell pro-inflammatory activation, inducing direct pathogenicity against the intestinal epithelium.
Collectively, we provided solid evidence that a strict crosstalk between the gut microbiota and the intestinal conventional and non-conventional T cells exists. Antibiotic-associated dysbiosis has immunostimulatory functions. Moreover, FMT can therapeutically control intestinal experimental colitis and this poses FMT as a valuable therapeutic option in immune-related pathologies. In addition, we generated fundamental knowledge about the pathogenic functions exerted by human intestinal iNKT cells upon the interaction with mucosa-associated microbiota communities
Abelian Gauge Potentials on Cubic Lattices
No full textThe study of the properties of quantum particles in a periodic potential subjected to a magnetic field is an active area of research both in physics and mathematics, and it has been and is yet deeply investigated. In this chapter we discuss how to implement and describe tunable Abelian magnetic fields in a system of ultracold atoms in optical lattices. After reviewing two of the main experimental schemes for the physical realization of synthetic gauge potentials in ultracold setups, we study cubic lattice tight-bindingmodels with commensurate flux.We finally discuss applications of gauge potentials in one-dimensional rings.</p
Thouless pumping in Josephson junction arrays
No full textRecent advancements in fabrication techniques have enabled unprecedented clean interfaces and gate tunability in semiconductor-superconductor heterostructures. Inspired by these developments, we propose protocols to realize Thouless quantum pumping in electrically tunable Josephson junction arrays. We analyze, in particular, the implementation of the Rice-Mele and the Harper-Hofstadter pumping schemes, whose realization would validate these systems as flexible platforms for quantum simulations. We investigate numerically the long-time behavior of chains of controllable superconducting islands in the Coulomb-blockaded regime. Our findings provide new insights into the dynamics of periodically driven interacting systems and highlight the robustness of Thouless pumping with respect to boundary effects typical of superconducting circuits
Detecting Majorana modes by readout of poisoning-induced parity flips
No full textReading out the parity degree of freedom of Majorana bound states is key to demonstrating their non-Abelian exchange properties. Here, we present a low-energy model describing localized edge states in a two-arm device. We study parity-to-charge conversion based on coupling the superconductor bound states to a quantum dot whose charge is read out by a sensor. The dynamics of the system, including the readout device, is analyzed in full using a quantum-jump approach. We show how the resulting signal and signal-to-noise ratio differentiates between local Majorana and Andreev bound states
Circulating extracellular vesicles as non-invasive biomarker of rejection in heart transplant
No full text[Formula presented] BACKGROUND: Circulating extracellular vesicles (EVs) are raising considerable interest as a non-invasive diagnostic tool, as they are easily detectable in biologic fluids and contain a specific set of nucleic acids, proteins, and lipids reflecting pathophysiologic conditions. We aimed to investigate differences in plasma-derived EV surface protein profiles as a biomarker to be used in combination with endomyocardial biopsies (EMBs) for the diagnosis of allograft rejection. METHODS: Plasma was collected from 90 patients (53 training cohort, 37 validation cohort) before EMB. EV concentration was assessed by nanoparticle tracking analysis. EV surface antigens were measured using a multiplex flow cytometry assay composed of 37 fluorescently labeled capture bead populations coated with specific antibodies directed against respective EV surface epitopes. RESULTS: The concentration of EVs was significantly increased and their diameter decreased in patients undergoing rejection as compared with negative ones. The trend was highly significant for both antibody-mediated rejection and acute cellular rejection (p < 0.001). Among EV surface markers, CD3, CD2, ROR1, SSEA-4, human leukocyte antigen (HLA)-I, and CD41b were identified as discriminants between controls and acute cellular rejection, whereas HLA-II, CD326, CD19, CD25, CD20, ROR1, SSEA-4, HLA-I, and CD41b discriminated controls from patients with antibody-mediated rejection. Receiver operating characteristics curves confirmed a reliable diagnostic performance for each single marker (area under the curve range, 0.727–0.939). According to differential EV-marker expression, a diagnostic model was built and validated in an external cohort of patients. Our model was able to distinguish patients undergoing rejection from those without rejection. The accuracy at validation in an independent external cohort reached 86.5%. Its application for patient management has the potential to reduce the number of EMBs. Further studies in a higher number of patients are required to validate this approach for clinical purposes. CONCLUSIONS: Circulating EVs are highly promising as a new tool to characterize cardiac allograft rejection and to be complementary to EMB monitoring
Embedded Streaming Principal Components Analysis for Network Load Reduction in Structural Health Monitoring
No full textPrincipal Component Analysis (PCA) is a well-established approach commonly used for dimensionality reduction. However, its computational cost and memory requirements hamper the adoption of PCA in heavily resource-constrained embedded platforms. Streaming approaches have been proposed that may enable embedded implementations of the PCA. Among them, the History PCA (HPCA) algorithm stands out for its robustness to the variability in parameters and accuracy. This paper presents a parallel and memory-efficient implementation of HPCA in a structural health monitoring (SHM) application based on a heterogeneous network with sensor nodes measuring three-axial accelerations and gateways collecting measurements from several nodes and sending them to the cloud storage and analytic facility. In the targeted application, standard PCA reaches 15× compression factor with an average reconstruction signal to noise ratio of 16 dB and a negligible impact on the accuracy in the tracking of structural modal frequencies. By embedding HPCA on our SHM network gateways, we achieve the same compression factor as standard PCA, with more than 1000× reduction in data memory footprint for running the algorithm. Furthermore, we parallelize HPCA on the gateway, and we achieve a speedup of 7.1× (on 8 cores). Finally, we explore a fixed-point HPCA implementation on sensors (network end-nodes), that maximally distributes compression workload, minimizes required communication bandwidth, and maintains the same quality of reconstruction as HPCA in floating-point, with a compression factor of 10×
Identification of a serum and urine extracellular vesicle signature predicting renal outcome after kidney transplant
No full textBACKGROUND: A long-standing effort is dedicated towards the identification of biomarkers allowing the prediction of graft outcome after kidney transplant. Extracellular vesicles (EVs) circulating in body fluids represent an attractive candidate, as their cargo mirrors the originating cell and its pathophysiological status. The aim of the study was to investigate EV surface antigens as potential predictors of renal outcome after kidney transplant. METHODS: We characterized 37 surface antigens by flow cytometry, in serum and urine EVs from 58 patients who were evaluated before, and at 10-14 days, 3 months and 1 year after transplant, for a total of 426 analyzed samples. The outcome was defined according to estimated glomerular filtration rate (eGFR) at 1 year. RESULTS: Endothelial cells and platelets markers (CD31, CD41b, CD42a and CD62P) in serum EVs were higher at baseline in patients with persistent kidney dysfunction at 1 year, and progressively decreased after kidney transplant. Conversely, mesenchymal progenitor cell marker (CD1c, CD105, CD133, SSEEA-4) in urine EVs progressively increased after transplant in patients displaying renal recovery at follow-up. These markers correlated with eGFR, creatinine and proteinuria, associated with patient outcome at univariate analysis and were able to predict patient outcome at receiver operating characteristics curves analysis. A specific EV molecular signature obtained by supervised learning correctly classified patients according to 1-year renal outcome. CONCLUSIONS: An EV-based signature, reflecting the cardiovascular profile of the recipient, and the repairing/regenerative features of the graft, could be introduced as a non-invasive tool for a tailored management of follow-up of patients undergoing kidney transplant
- …
